Paper published in IEEE Access

A paper titled “Physics-Informed Data-Driven Modeling of HVAC Systems: A Systematic Analysis” by Nam T. Nguyen, Binh Nguyen, and Truong X. Nghiem has been published in IEEE Access. The paper studies physics-informed machine learning techniques that integrate monotonicity, boundedness, and system structure for HVAC system identification, and systematically benchmarks thirteen gray-box, physics-agnostic, and physics-informed models using real-world HVAC data. Results show that physics-informed approaches provide more accurate and robust temperature-dynamics prediction, especially under limited, unreliable, or noisy training data.